1. Introduction
The global surge in CO
2 emissions contributes to severe environmental and economic challenges, including global warming, rising sea levels, species depletion, and extreme weather events. The transportation sector, particularly road transportation, is a significant source of global CO
2 emissions [
1,
2,
3] and faces the greatest pressure to reduce these emissions. Therefore, new energy vehicles (NEVs) with low-carbon and environmentally friendly characteristics have become a key strategy for alleviating global CO
2 emissions and environmental issues. In March 2024, the International Energy Agency (IEA) indicated in its “CO
2 Emissions in 2023” report that carbon emissions from the transport sector experienced the most pronounced growth, surging by nearly 240 Mt globally in 2023 [
4] (p. 19). Without the growing deployment of five key clean energy technologies, including NEVs, since 2019, the rise in global CO
2 emissions would have been three times higher [
4] (p. 6). Simulative studies have shown that deploying 1% more electric vehicles can reduce carbon emissions by 0.5–0.9% [
5,
6,
7]. In this context, promoting NEVs and related industries to replace traditional fossil fuels has a profound impact on optimizing global energy structures and reducing carbon emissions [
8,
9].
Green technological innovation in the NEV industry is considered to be the core driving force for CO
2 emissions reduction and achieving stable, sustainable development [
10,
11,
12]. Green technological innovation refers to the innovation of technological capabilities that reduce carbon emissions and improve energy utilization efficiency in a company’s core operations [
13,
14,
15,
16]. This includes initiatives in energy savings, curbing pollution, reprocessing waste, designing eco-friendly products, and managing environmental impacts. Furthermore, green technological innovation competes with traditional technological innovation, which prioritizes economic benefits in the short term but promotes development alongside technological advancement in the long term. Green technological innovation can accelerate industry restructuring, extend the value of industrial chains, and improve labor productivity through the integration of multistage innovative knowledge and capabilities, contributing to the sustainability of the entire industry. Therefore, with the aid of green technological innovation, the NEV industry is expected to further enhance energy utilization efficiency and reduce carbon emissions in the production, transmission, and consumption processes of the entire supply chain, thereby achieving both environmental and economic benefits.
However, green technological innovation is currently facing unprecedented challenges due to economic policy uncertainty (EPU) [
17,
18,
19,
20]. In recent years, owing to the impact of a sluggish international market, trade protectionism, anti-globalization economic trends, and public health security concerns, governments worldwide have intervened in the market by formulating and implementing a series of economic stimulus policies [
21,
22]. While these policies prevented a sharp economic downturn, the frequent adjustment of macro-policy environments led to ambiguous and unpredictable government policy directions, resulting in significant EPU [
23,
24]. In China, the annual average value of the EPU index in 2020 was 13.39 times that in 2000 [
25], leading to increased risks in market operations, exacerbated capital outflows, and economic turbulence [
26]. Such factors affect enterprises’ sensitivity to economic policies and influence their technological innovation activities. This influence is particularly evident in the NEV industry, which is heavily reliant on government support, where the impact of EPU on green technological innovation becomes pronounced. China, as the world’s largest carbon emitter, actively promotes its domestic NEV industry through strategies such as financial subsidies and tax incentives [
27,
28]. According to data from the China Automotive Industry Association, NEV production volume in China amounted to 9.442 million units in 2023 [
29]. As shown in
Figure 1, the market share of NEVs in China has steadily increased year by year, reaching 31.55% in 2023, with NEVs accounting for 6.07% of the total vehicle stock. This surge in NEV consumption contributed to a global reduction of approximately 50 million tons of carbon emissions [
30], significantly aiding emissions mitigation within the transportation sector. In this context, studying the impact of EPU on green technological innovation in Chinese NEV enterprises holds practical significance.
Limited research has been dedicated to exploring the effect of EPU on green technological innovation in NEV enterprises. Current scholarly works primarily focus on the relationship between green technological innovation and EPU, but no consensus has been reached [
17,
18,
24,
31], reflecting the profound complexity of this field. Compared to general industries, the NEV industry is in a rapid development stage with competitive market incentives and high policy environment dependence [
10,
11,
32]. Moreover, compared to general technological innovation, green technological innovation involves large amounts of R&D resources, has long investment cycles, and bears higher risks [
33,
34,
35], typically generating direct economic benefits over the long term. This implies greater uncertainty and more externalities for businesses. These characteristics create a clear distinction between the green technological innovation of NEV enterprises and that of general industries or typical technological innovation activities, necessitating further study. However, no study has specifically focused on the activities related to green technological innovation of NEV enterprises under EPU.
Therefore, this study examines the relationship between EPU and green technological innovation in Chinese NEV enterprises using panel data on green technological innovations of listed companies between 2012 and 2022. In addition, considering the constraints of internal resources and the external institutional environment on corporate innovation, we also analyzed the moderating effects of corporate ESG performance and government subsidies on this relationship.
The potential marginal contributions of this study are as follows: First, by focusing on the impact of EPU on green technological innovation in NEV enterprises, this study broadens the scope of existing research on NEV enterprises. Existing literature primarily focuses on aspects such as market demand, technological advancement, and the policy impact of NEVs [
27,
28,
36,
37]. Through an EPU examination, this study further uncovers the influence of the policy environment on green technology innovation in NEV enterprises, which is important for understanding how businesses innovate in uncertain environments.
Second, by exploring the moderating roles of corporate ESG performance and government subsidies from the perspective of internal corporate resources and external government policy incentives, this study enriches and expands the boundary conditions under which EPU affects green technological innovation in the NEV industry. Existing research indicates that corporate green technological innovation is a complex systemic issue constrained by the external institutional environment and internal resource conditions [
33,
34,
35,
38]. Thus, this study clarifies the role of corporate ESG performance and government subsidies in mitigating the effects of EPU on green technological innovation while providing a new perspective for understanding and guiding green technological innovation.
Finally, in further analysis, this study considers factors such as regional public environmental concern [
39] and NEV company types (whole vehicle manufacturers versus parts producers) [
40] and their roles in the relationship between EPU and corporate green technological innovation. The findings suggest that the social environments in different regions and types of businesses may have varying impacts on the relationship between NEV enterprises’ EPU and green innovation.
The rest of this article is structured as follows: The second section reviews relevant previous research, the third section proposes the research hypotheses of the study, the fourth section describes the methodology, the fifth section presents the results of the empirical tests, and the sixth section concludes the study.
5. Results
5.1. Descriptive Statistics
Table 2 presents the descriptive statistics of the main variables. The mean value of GTI among Chinese NEVs was 12.379, with a standard deviation of 44.434, ranging from 0 to 526. This indicates significant differences in green technological innovation among Chinese NEV enterprises. Among the explanatory variables, the average value of EPU is 4.198, with a standard deviation of 2.374, ranging from 1.139 to 7.919, indicating some variability in China’s EPU over different periods.
Moreover, the multicollinearity test of the model showed that the average variance inflation factor (VIF) of the variables selected in this study was 2.15 (below the conventional threshold of 6), and the maximum VIF value was 4.17 (below the conventional threshold of 10). Therefore, multicollinearity was not a concern.
5.2. Cross-Sectional Dependence (CD) Test
Before proceeding with panel data analysis, it is essential to test for cross-sectional dependence. We employ two testing methods: the Baltagi–Pinnoi LM test and the Pesaran CD test [
69,
70]. The results, presented in
Table 3, show that both tests reject the null hypothesis of “no cross-sectional dependence” at a 1% significance level. Therefore, it is necessary to consider the impact of cross-sectional dependence in the subsequent empirical analysis.
5.3. Regression Analysis
The dependent variable in this study is the green technological innovation of Chinese NEV enterprises, as measured by the number of corporate green patent applications. For this count variable, Poisson and negative binomial regressions are appropriate econometric methods. However, a limitation of the Poisson regression is the Poisson distribution characteristic, in which the mean equals the variance, which does not conform to our actual data. The standard deviation of the dependent variable in this study was 44.434, with a mean of 12.739, making the variance more than a hundred times the mean. This indicates a potential overdispersion. Although the panel Poisson regression remains consistent even with overdispersion, a negative binomial regression may be more efficient. Hence, we used a panel negative binomial regression model for data analysis. In the robustness check, Poisson regression was used as an alternative testing method.
After selecting the panel negative binomial model, we conducted both the Likelihood Ratio (LR) test and the Hausman test to ascertain the appropriate model specification among mixed-effects, fixed-effects, or random-effects. The LR test significantly rejected the null hypothesis of mixed effects at the 1% significance level. Similarly, the Hausman test significantly rejected the null hypothesis of random effects at the 1% level, prompting us to adopt a fixed-effects model. The fixed-effects panel negative binomial regression offers the advantage of estimating the coefficients for variables that are invariant over time, showcasing a key benefit of this methodological approach.
Table 4 presents the results of the basic linear regression analysis. In
Table 4, the first column includes only the control variables, and the second column adds EPU to the control variables. The regression results in the second column indicate that the coefficient for EPU is significantly negative at the 5% level, which implies that as EPU increases, green technological innovation in Chinese NEV enterprises decreases significantly, which validates H1.
Columns 3 to 5 test the moderating roles of corporate ESG and government subsidies. Columns 3 and 4 include the interaction terms EPU×ESG and EPU×Gsub, respectively. Column 5 includes this information in the test model. The test results in Columns 3–5 show that the coefficient of the EPU×ESG interaction term is significantly positive, indicating that corporate ESG significantly mitigates the negative impact of EPU on green technological innovation in NEV enterprises. In other words, the higher the corporate ESG of NEV enterprises, the lower the negative impact of EPU on their green technology innovation, confirming H2; the coefficient of the EPU×Gsub is significantly positive, demonstrating that government subsidies significantly weaken the negative impact of EPU on green technology innovation in NEV enterprises. That is, the more government subsidies NEV enterprises receive, the less negative the impact of EPU on their green technological innovation, supporting H3.
5.4. Endogeneity and Robustness Tests
5.4.1. Discussion of Endogeneity Issue
Although EPU is an exogenous macroeconomic variable with a lower possibility of sample selection bias and causality issues, endogeneity can still be introduced by measurement errors. Therefore, we use the instrumental variable two-stage least squares (IV-2SLS) method to mitigate the issue of endogeneity. Building on the methodology of Zhong et al. [
31], we selected U.S. EPU (IV-EPU) as the instrumental variable for China’s EPU. The macroeconomic policies of China and the United States are highly correlated, satisfying the assumption of relevance for the instrumental variable; however, U.S. EPU does not directly affect the green technological innovation activities of Chinese NEV enterprises, meeting the exogeneity condition of the instrumental variable. Additionally, a weak instrument variable test revealed an F-value of 368.52 (far greater than 10) and a
p-value of 0.000, suggesting that there was no weak instrument variable problem.
Table 5 presents the regression model using the U.S. EPU index as an instrumental variable, which shows that after using the U.S. EPU index as an instrumental variable, EPU still had a significant negative impact on the green technological innovation of Chinese NEV enterprises, indicating that the baseline conclusions of this study remain valid even after addressing potential endogeneity issues.
5.4.2. Robustness Tests
To assess the robustness of the main findings, we replaced the dependent variable with both the test model and measurement indicators. First, a mixed Poisson regression was used as an alternative method for robustness testing. The test results in
Table 6 are consistent with those in
Table 4, further verifying the core findings’ robustness.
Table 6 presents a pseudo R
2 of approximately 0.45, while
Table 4 reports a notably lower pseudo R
2 of about 8%. The significant difference in pseudo R
2 between the mixed Poisson regression and the fixed effects negative binomial regression could be attributed to the following reasons: (1) As previously mentioned, the dependent variable exhibits overdispersion. The mixed Poisson regression, by incorporating random effects, may partially account for unobservable heterogeneity, which could inflate the model’s R
2. However, this model may not fully account for the overdispersion present in the data. (2) The fixed effects negative binomial regression is equipped to handle overdispersion in panel data and controls for unobserved individual-specific invariant characteristics through fixed effects. However, if there is a correlation between individual effects and explanatory variables, the fixed effects model might absorb some of this explanatory power, leading to a lower R
2.
Second, green invention patent applications (GTI2) and green utility model patent applications (GTI3) are used as alternative measurement indicators for the explained variable of green technology innovation. According to the application standards, green patents are classified into categories encompassing inventive patents, utility models, and design patents [
17,
18,
46]. The endorsement process for inventive green patents is notably more intricate and protracted compared to that for green utility model patents. In robustness testing, further dividing the number of green patent filings into counts of invention and utility model patents allows for further investigation of the effects of EPU on sustainable innovation within NEV corporations. The regression results in
Table 7 and
Table 8 show that after replacing the measurement indicators for the explained variables, and despite minor differences in the coefficients for EPU, the coefficients of EPU×ESG and EPU×Gsub remain consistent with those in
Table 4, supporting our findings.
5.5. Heterogeneity Analysis
Given the differences in public environmental awareness across provinces and among types of NEV enterprises (vehicle manufacturers or parts producers), the impact of EPU on green technological innovation, as well as the moderating effects of corporate ESG and policy subsidies, may vary significantly.
First, environmental awareness represents the public’s environmental preferences in each province, which significantly influences the social resource support that NEV enterprises can obtain for green technological innovation. The literature indicates that profit-maximizing companies often lack motivation for active environmental engagement, necessitating government policy intervention [
59,
71]. However, market-based and informal regulatory oversight at the public level has not yet been effective [
72]. To further explore the potential impact of public environmental preferences, we used the Baidu Smog Search Index to analyze the effects of environmental attention in different regions.
The Baidu Smog Search Index was chosen to represent public environmental attention for the following reasons:
- (1)
Baidu, as the largest Chinese search engine, has wide coverage and data accessibility, thus allowing for data analysis in all regions of China based on search frequency and location.
- (2)
Compared to other environmental issue keywords, such as “environmental pollution”, smog weather offers higher environmental perceptibility, enabling the public to gauge its severity through air visibility.
The term “smog” attracts significant public attention, reflecting widespread environmental concern. The Baidu Index is categorized into three types: overall search index, PC search index, and mobile search index, where the overall search index is the weighted sum of the PC and mobile search indices. This study chose the keyword “smog” not only because smog weather has strong environmental perceptibility but also because the correlation coefficient between PM2.5, pollution concentration, and the Air Quality Index (AQI) is as high as 0.9267, making smog pollution a good indicator of air quality [
39].
This study classified Chinese provinces into areas with high and low public environmental awareness based on the average Baidu Smog Search Index, conducting subsample tests according to company registration locations. Columns 1–2 in
Table 9 present the regression results. The results show no significant differences in the impact of EPU or the moderating role of corporate ESG on green technological innovation in NEV enterprises between provinces with varying degrees of public environmental awareness. However, significant heterogeneity exists in the moderating effects of policy subsidies. In provinces with higher public environmental awareness, the moderating effect of government subsidies on the EPU-green technological innovation relationship in NEV enterprises is weaker compared to provinces with lower awareness, suggesting that social resource support and government subsidies can substitute for each other to a certain extent when facing resource constraints caused by EPU. In provinces with high public environmental awareness, NEV enterprises’ green technological innovation activities receive more social resource support, which helps reduce their dependence on government subsidies.
Second, vehicle manufacturers and parts producers differ in their positions within the industry chain and the roles they play in green technological innovation within the industry [
40,
67], and the relationship between EPU and green technological innovation may vary accordingly. Columns 3–4 in
Table 9 show the regression results from the subsample testing based on vehicle manufacturers and NEV parts producers. The results show that, although there is no significant difference in the impact of EPU and the moderating role of corporate ESG on green technological innovation for both types of companies, there is clear heterogeneity in the moderating role of government subsidies. Specifically, compared to that of parts producers, government subsidies have a more pronounced moderating effect on green technological innovation for vehicle manufacturers, which may be related to the core position of vehicle manufacturers in the industry chain. Vehicle manufacturers not only assemble vehicles but also work with suppliers, dealers, and service providers to jointly promote the industry’s green transformation, which requires large-scale funding. Therefore, when faced with the EPU, green technological innovation among NEV manufacturers relies more on government subsidies.
6. Discussion
This paper aims to explore the impact of EPU on green technological innovation within NEV companies. As EPU has risen significantly worldwide, scholars such as Zhong et al. and Guan et al. [
24,
31] have dedicated considerable time and effort to investigating its effects on corporate innovation. However, most existing research has concentrated on the overall innovation activities of companies rather than specifically on the green technological innovation of NEV companies, and no consensus has yet been reached. We delve into the unique characteristics of the NEV industry and differentiate between green technological innovation and general corporate innovation activities, offering more nuanced theoretical insights into the relationship between EPU and corporate innovation. The NEV sector is rapidly evolving and heavily reliant on government policy support, distinguishing it from more established industries. Green technological innovation demands more extensive R&D investments, longer development cycles, and entails greater risks than traditional technological advances. Thus, it is particularly susceptible to EPU. EPU’s increase may significantly reduce NEV companies’ expectations of future government incentives and exacerbate challenges in acquiring and leveraging R&D resources in the market. Consequently, NEV companies may opt to direct their limited R&D resources towards projects with immediate economic returns, as opposed to long-term green technological innovations. Our findings indicate a significant decrease in green technological innovation within NEV companies with the escalation of EPU, an observation with significant implications for how governments and businesses can counteract the adverse effects of EPU and foster green technological development in the NEV sector.
Additionally, we examine the moderating role of corporate ESG performance and government subsidies on the relationship between EPU and green technological innovation in NEV companies, considering both the internal resource conditions of the company and external policy incentives from the government. ESG performance, as extensively analyzed in the literature [
47], is a critical indicator of a company’s capabilities for green development. We propose that robust ESG performance can alleviate the detrimental effects of EPU on green technological innovation in NEV firms. A strong ESG record may decrease information asymmetry between companies, the government, and the public, thus enabling NEV companies to secure resource support more effectively, which in turn mitigates the resource limitations imposed by EPU. Furthermore, a proactive ESG strategy can enhance a company’s internal operational efficiency, allowing for better integration and utilization of resources. Therefore, the higher the ESG rating of a NEV company, the less pronounced the negative impact of EPU on its green technology innovation endeavors.
In contemporary research on NEV enterprises, it is widely held among scholars that the NEV industry’s growth is significantly influenced by government policy support, especially financial subsidies [
5,
11]. Our findings align with these scholarly perspectives, showing that amidst escalating EPU, substantial government subsidies are crucial in mitigating the challenges NEV enterprises face, such as the lack of external investment incentives and constraints on internal innovation resources, during their green technological innovation endeavors. These government subsidies not only diminish apprehensions regarding policy volatility but also bolster the confidence of NEV enterprises to pursue green technological innovations. Furthermore, such subsidies provide a cushion against the financing strains that arise from heightened EPU. Therefore, it is evident that the extent of government subsidies received by NEV enterprises inversely correlates with the adverse effects of EPU on their green technological innovation efforts.
Our heterogeneity analysis delves into the influence of public environmental concern and the categorization of NEV enterprises—whether they are whole vehicle manufacturers or parts producers—on the interplay between EPU and the green technological innovation within these enterprises. Public environmental concern, as a reflection of societal environmental preferences across various provinces, plays a pivotal role. Echoing Porter’s hypothesis, we posit that environmental regulations compel firms to innovate, thereby neutralizing the costs associated with environmental compliance. He et al.’s [
73] investigation into the Dual Credit Policy (DCP) underscores this, highlighting its significant influence on the Total Factor Productivity (TFP) of NEV enterprises. Our study further suggests that societal environmental preferences directly impact the green technological initiatives of NEV enterprises. In regions marked by heightened public environmental concern, these enterprises gain increased support from social resources for their green innovation activities, diminishing their reliance on government subsidies. In contrast, in areas with strong public environmental awareness, the moderating role of government subsidies on the nexus between EPU and green technological innovation is comparatively less pronounced.
Additionally, our analysis reveals that government subsidies exert a more substantial moderating effect on the green technological innovation of whole vehicle manufacturers than on parts producers. Our findings diverge from those of Wu et al. and Ren and Liu [
40,
67], who focus on the stimulating effects of government subsidies on R&D innovation within NEV enterprises from an industrial chain perspective. Focusing more acutely on green technological innovation, our study acknowledges that such innovation is resource-intensive, with a protracted investment horizon and elevated risks. Whole vehicle manufacturers, in contrast to parts producers, are tasked not only with vehicle assembly but also with fostering collaboration among parts suppliers, dealers, service providers, and other stakeholders to spearhead the industry’s green transition. Consequently, the reliance of whole-vehicle NEV enterprises on government subsidies has intensified in response to rising EPU, underscoring the subsidies’ role in supporting sustainable industry innovation.
The negative impact of EPU on green technological innovation within NEV enterprises in China is evident. Reducing the negative effects of EPU is crucial for achieving China’s “3060 Goals” and “Made in China 2025” strategy. Green technology innovation in NEV enterprises not only promotes the transformation and upgrading of China’s automotive industry but also has a profound and positive impact on the modernization, intelligence, and greening of the entire manufacturing industry. This supports the achievement of both the “Made in China 2025” strategy and the goals of carbon peak and carbon neutrality. Firstly, the development of the NEV industry, being a technology-intensive field, necessitates continual research and innovation. Upgrading core technologies such as power batteries, motors, and electronic controls can advance the technological progress of the entire automotive manufacturing industry, aligning with the “Made in China 2025” emphasis on high-end manufacturing and innovation capabilities. By developing batteries with higher energy density, longer life, and greater recyclability and adopting more efficient electric drive systems and energy recovery technologies, the carbon footprint of NEVs can be reduced. Secondly, NEV enterprises can implement green manufacturing processes and build efficient, green, and transparent supply chain systems. This not only reduces costs but also ensures the environmental friendliness and sustainability of the entire production process, which is an important aspect of the green development goals outlined in “Made in China 2025.” Thirdly, through green innovation, NEV enterprises can establish environmentally friendly brand images and accelerate the popularization of NEVs. This can lead to the large-scale replacement of traditional fuel vehicles, thereby reducing overall greenhouse gas emissions. This aligns with the brand-building strategies outlined in “Made in China 2025”.